Collision Selective Visual Neural Network Inspired by Neurons in - - PowerPoint PPT Presentation

Collision Selective Visual Neural Network Inspired by Neurons in Juvenile Locusts

Qinbing Fu and Shigang Yue Computational Intelligence Lab School of Computer Science

supported by EU FP7-IRSES Project EYE2E (269118) and LIVCODE (295151)

Research Interests

• Neuroscience: Neural Networks (SNN), Visual

Pathway (ON/OFF)

• Computer Vision: Bio-inspired Visual NN (LGMDs,

EMDs, DSNs, STMDs) – Motion Detectors inspired by insects

• Robotic Application (Colias)
• Machine Learning (CNN, DL)

Motivations

• To simulate the juvenile locusts on the ground

responding to the predators from the sky

• To realize particular collision selectivity, btw

solve the defects of LGMD1 computational models.

• A robust and cheap looming objects detector

LGMDs Morphology

Network

Core

• Low-level Image Feature Processing
• Differential Image
• No object recognition, scene analysis, even edge detection
• A Biased ON and OFF Dual-Channel
• ON – luminance increments, OFF – luminance decrements
• Separate signal flows, filter, then fuse
• A game race between Excitatory and Inhibitory flows
• Only sensitive to Light-to-Dark luminance change
• Highly Non-linear Signal Processing
• Outputs – Firing Rates of Exponential Distribution

EXPs

Simulated Stimulus

EXPs

Simulated Stimulus

Recorded Stimulus

Colias Tests

What is Colias seeing?

In Arena Watch Videos

Continue……

Colias Feedback

Continue……

Distance to Detect Collision

Conclusion

• Perform well in arena with many obstacles
• Able to detect looming objects against bright

background selectively while not responding to

• bjects in dark background
• Practical for ground mobile robots and Cheap cost

Future Work

• ON/OFF for Directional Motion Detectors
• Data Training
• SNN

Thank you!